Composite graphs showing the following: Descriptor variation along the regions before, at and after the analysed PSSE; the reliability value (or % of helical structure at each loci) and the p-value for the descriptor: Number of contacts, type “HBMM”. Data are drawn from the datamart containing PSSEs of length = 12 AARs; the consensus definition of a helix element is from “PDB-DSSP-Stride”, and the redundancy is 70% similarity at the sequence level.

<p>Composite graphs showing the following: Descriptor variation along the regions before, at and after the analysed PSSE; the reliability value (or % of helical structure at each loci) and the p-value for the descriptor: Number of contacts, type “HBMM”. Data are drawn from the datamart containing PSSEs of length = 12 AARs; the consensus definition of a helix element is from “PDB-DSSP-Stride”, and the redundancy is 70% similarity at the sequence level.</p

The KS test applied for sliding windows in all size helices using single parameter analysis.

<p>The KS test applied for sliding windows in all size helices using single parameter analysis.</p

Study of specific nanoenvironments containing α-helices in all-α and (α+β)+(α/β) proteins

<div><p>Protein secondary structure elements (PSSEs) such as α-helices, β-strands, and turns are the primary building blocks of the tertiary protein structure. Our primary interest here is to reveal the characteristics of the nanoenvironment formed by both PSSEs and their surrounding amino acid residues (AARs), which might contribute to the general understanding of how proteins fold. The characteristics of such nanoenvironments must be specific to each secondary structure element, and we have set our goal here to gather the fullest possible description of the α-helical nanoenvironment. In general, this postulate (the existence of specific nanoenvironments for specific protein substructures/neighbourhoods/regions with distinct functionality) was already successfully explored and confirmed for some protein regions, such as protein-protein interfaces and enzyme catalytic sites. Consequently, PSSEs were the obvious next choice for additional work for further evidence showing that specific nanoenvironments (having characteristics fully describable by means of structural and physical chemical descriptors) do exist for the corresponding and determined intraprotein regions. The nanoenvironment of α-helices (nEoαH) is defined as any region of the protein where this secondary structure element type is detected. The nEoαH, therefore, includes not only the α-helix amino acid residues but also the residues immediately around the α-helix. The hypothesis that motivated this work is that it might in fact be possible to detect a postulated “signal” or “signature” that distinguishes the specific location of α-helices. This “signal” must be discernible by tracking differences in the values of physical, chemical, physicochemical, structural and geometric descriptors immediately before (or after) the PSSE from those in the region along the α-helices. The search for this specific nanoenvironment “signal” was made possible by aligning previously selected α-helices of equal length. Afterward, we calculated the average value, standard deviation and mean square error at each aligned residue position for each selected descriptor. We applied Student’s t-test, the Kolmogorov-Smirnov test and MANOVA statistical tests to the dataset constructed as described above, and the results confirmed that the hypothesized “signal”/“signature” is both existing/identifiable and capable of distinguishing the presence of an α-helix inside the specific nanoenvironment, contextualized as a specific region within the whole protein. However, such conclusion might rarely be reached if only one descriptor is considered at a time. A more accurate signal with broader coverage is achieved only if one applies multivariate analysis, which means that several descriptors (usually approximately 10 descriptors) should be considered at the same time. To a limited extent (up to a maximum of 15% of cases), such conclusion is also possible with only a single descriptor, and the conclusion is also possible in general for up to 50–80% of cases when no less than 5 nonlinear descriptors are selected and considered. Using all the descriptors considered in this work, provided all assumptions about data characteristics for this analysis are met, multivariate analysis regularly reached a coverage and accuracy above 90%. Understanding how secondary structure elements are formed and maintained within a protein structure could enable a more detailed understanding of how proteins reach their final 3D structure and consequently, their function. Likewise, this knowledge may also improve the tools used to determine how good a structure is by means of comparing the “signal” around a selected PSSE with the one obtained from the best (resolution and quality wise) protein structures available.</p></div

Variation in the number of descriptors that passed both the normal distribution test and the no mutual correlation test for different helix sizes.

<p>Variation in the number of descriptors that passed both the normal distribution test and the no mutual correlation test for different helix sizes.</p

Comparison of the average values of 8 descriptors, normalized (by inverse coefficient of variation) done by dividing the parameter values with the corresponding standard deviation, and calculated for regions inside (17 AAARs) and outside the PSSE.

<p>The following descriptors are likely to show the postulated “signal” (the differences between the inside and outside descriptor values per position are higher than 1): 1. Hbmm, 15. Hbmm_WNADist, 29. Hbmm_WNASurf, 61. Number_Unused_Contact_WNADist, 62. Number_Unused_Contact_WNASurf, 63. Dihedral_Angle_PHI, 64. Dihedral_Angle_PSI, 66. Density. The two shadowed descriptors are expected to show differences, as these descriptors are basically part of the definition of the investigated PSSE.</p

An example of an α-helix (in a specific (α+β) protein) and its nanoenvironment: The synthetic gene encoded DcpS bound to the inhibitor DG157493 (3bl9.pdb) has fourteen α-helices, and each helix has its own nanoenvironment.

<p>Highlighted inside the transparent spheres is an α-helix (ribbon, purple). The nanoenvironment includes the amino acid residues of the α-helix and the amino acid residues around the helix that are within reach of the probing sphere, whose radius was previously selected. The pre- and postregions (extension by 32 AARs each) are not shown here for the sake of clarity of the basic definition.</p